The MNIST (CC BY-SA 3.0) images are reduced to an 8-D embedding using a non-linear dimensionality reduction algorithm called UMAP [1]} with default settings. Synthetic data are obtained by sampling from a pre-trained MMD GAN [2]} (BSD-3-Clause). Samples are automatically labeled by training a classifier obtained from https://github.com/pytorch/examples/tree/master/mnist (BSD-3-Clause) and taking its predictions on the samples. Each party \(i\)  has a dataset \(D_i\)  of size 5K, and \(|G|=100\) K synthetic data points are generated.
